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The comparison of GLCM and granulometry for distinction of different classes of urban area

机译：GLCM和粒度区别区别不同类别城市地区的比较

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摘要

The paper describes the possibilities of using selected methods of texture analysis - Grey Level Co-occurrence Matrix (GLCM) and granulometry analysis - to isolate different classes of urban areas in the process of semi-automatic classification. To evaluate the effectiveness of each method the Jeffries-Matusita Distance method was applied. The study was conducted on the SPOT5 HRG (pixel 10m resolution) images. The research shows the high efficiency of the Haralick Correlation (one of GLCM features) method and granulometric analysis, in particular using its multiscaling.

机译：本文介绍了使用所选纹理分析方法 - 灰度级共发生矩阵（GLCM）和粒度分析的可能性 - 在半自动分类过程中隔离不同类别的城市地区。为了评估每种方法的有效性，应用Jeffries-Matusita距离方法。该研究在Spot5 HRG（像素10M分辨率）图像上进行。该研究表明了Haralick相关性的高效率（GLCM特征之一）方法和粒度分析，特别是使用其多尺度。

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来源
《Joint Urban Remote Sensing Event》|2017年|1 v.|共4页
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作者
Przemys?aw Kupidura; Inna Uwarowa;
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原文格式 PDF
正文语种
中图分类遥感技术;
关键词
Urban areas; Correlation; Satellites; Image color analysis; Buildings; Entropy; Context;

机译：城市地区;相关;卫星;图像颜色分析;建筑物;熵;背景;

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The comparison of GLCM and granulometry for distinction of different classes of urban area

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